Number of cluters of Kmedoids

Kmedoids_clusterN(dt)
## Loading required package: Matrix
## 
## Attaching package: 'Matrix'
## The following objects are masked from 'package:tidyr':
## 
##     expand, pack, unpack
Kmedoids_clusterN(dt, cluster = "Kmedoids-dr")
Kmedoids_gap(dt)
gap <- Kmedoids_gap(dt)
gap %>% group_by(representor, n_gap) %>% count()
gap %>% group_by(representor, n_gap, n_cluster) %>% count()
visualizeDistance(dt_orig, "ts-dr", "euclidean", "Kmedoids-dr")

inspect_silhouette(dt_orig, "ts-dr")
## Silhouette of 304 units in 2 clusters from pam(x = distance_mat, k = nl$other[[idx]]$n_cluster, diss = TRUE) :
##  Cluster sizes and average silhouette widths:
##        96       208 
## 0.2744682 0.1170225 
## Individual silhouette widths:
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
## -0.13004  0.08454  0.16717  0.16674  0.23687  0.46308 
## Silhouette of 304 units in 2 clusters from pam(x = distance_mat, k = nl$other[[idx]]$gap, diss = TRUE) :
##  Cluster sizes and average silhouette widths:
##        96       208 
## 0.2744682 0.1170225 
## Individual silhouette widths:
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
## -0.13004  0.08454  0.16717  0.16674  0.23687  0.46308
visualizeDistance(dt_orig, "error-dr", "euclidean", "Kmedoids-dr")

inspect_silhouette(dt_orig, "error-dr")
## Silhouette of 304 units in 49 clusters from pam(x = distance_mat, k = nl$other[[idx]]$n_cluster, diss = TRUE) :
##  Cluster sizes and average silhouette widths:
##             8             5             8             5             9 
## -0.0003233759  0.0642459925  0.0175961024  0.1302537990  0.0645456207 
##             9            13             9            13             9 
##  0.0550309839  0.0798515936  0.1143859251  0.0032087795  0.1120194038 
##            10             5             7             9             5 
##  0.0841089927  0.0672518287 -0.0033625277  0.0822341671  0.1005159138 
##             7             4             3            13            11 
##  0.0707567972  0.1571899674  0.2028535209  0.0616673475  0.0660769388 
##             7             3             8             3            10 
## -0.0111813626  0.0646849562  0.0594193291  0.1162582106  0.0447347694 
##             6             4             3             8            11 
##  0.0686855074  0.1352544873  0.3883743966 -0.0008835409  0.1744201361 
##             4             4             5             8             6 
##  0.2127076091  0.1641017615  0.0890566902  0.0676796763  0.1833597116 
##             8             3             3             8             3 
##  0.1078585326  0.1805205853  0.1675978695  0.0509553273  0.1203939402 
##             1             8             4             2             2 
##  0.0000000000  0.0867140835  0.0584048609  0.2648512547  0.3465482360 
##             3             3             1             3 
##  0.2503619440  0.1889673601  0.0000000000  0.3238241455 
## Individual silhouette widths:
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## -0.223677  0.005535  0.084396  0.089108  0.165337  0.468560
visualizeDistance(dt_orig, "accuracy", "euclidean")

Group Visualize

visualizeGroup(dt_orig, "error-dr", "euclidean", cluster= "Kmedoids-dr",names = dt_names)

visualizeGroup(dt_orig, "ts-dr", "euclidean", cluster= "Kmedoids-dr",names = dt_names)

visualizeGroup(dt_orig, "ts.features-dr", "euclidean", cluster= "Kmedoids-dr",names = dt_names)

visualizeGroup(dt_orig, "error.features-dr", "euclidean", cluster= "Kmedoids-dr",names = dt_names)

Overall statistics

avg_measure_fn(dt, metric = "rmsse") %>% arrange(bottom)

Overall rank mcb test

rank_compare(dt, filter_random = TRUE)
## Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
## ℹ Please use `all_of()` or `any_of()` instead.
##   # Was:
##   data %>% select(measure)
## 
##   # Now:
##   data %>% select(all_of(measure))
## 
## See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Registered S3 method overwritten by 'quantmod':
##   method            from
##   as.zoo.data.frame zoo